Soil Salinity:
Salt affected soils are caused by excess accumulation of salt typically most pronounced at the soil surface. Salt is often derived from geological formations featuring shale, marl, limestone, sylvite, gypsum, and halite. Variability of soil salinity is affected by parent material, soil type, and landscape position [10]. Moreover, salts can be transported to the soil surface by capillary action from brackish water tables and accumulated due to evaporation. They can also accumulate as a result of anthropogenic activities such as fertilization or oil production. Soil salinity is generally measured via electrical conductivity (EC) in soil saturated paste (ECp), its liquid extract (ECe), or using different soil to water suspensions [34]. Soil with an ECe greater than 4 dS m−1, is referred to as saline [7]. Plant tolerance of salinity is species specific, but values greater than 4 dS m−1 constrain the growth of many agronomic crops. Developed in the mid-1950s, ECe is one of the most widely reported soil quality assessment parameters [2, 22, 35]. Regular monitoring of soil salinity is essential for efficient soil and water management and sustainability of agricultural lands [5], especially in arid and semiarid environments. Yet, traditional laboratory methods for soil salinity characterization can be laborious and costly. Additionally, many soils are highly spatially variable, with salinity changing rapidly over small distances relative to topography and other factors [29]. Thus, a large number of soil samples are often required to adequately characterize soil salinity across landscapes.
To alleviate the cost of extensive sampling, previous studies have used remotely sensed (RS) data/imagery for detecting soil salinity. Specifically, soil salinity is related to different spectral bands, ratios, and parameters extracted from satellite imagery using soil and vegetation based indices [14, 15, 21, 23, 40]. While promising, these methods are limited by factors such as spatial and spectral resolution of the image, vegetation coverage, and atmospheric effects ([16]; Metternich and Zinck, 2008). One of the newest RS satellites available for research is Landsat 8, which was launched on Feb. 11, 2013. Landsat 8 orbits the entire Earth every 16 days in an 8-day offset from Landsat 7. The collected data is orthorectified and available to download at no charge. Landsat 8 carries two different instruments: 1) the operational land imager (OLI) sensor involves refined heritage bands, and 2) the thermal infrared sensor (TIRS) provides two thermal bands. Both sensors supply improved signal-to-noise (SNR) radiometric performance quantized over a 12-bit dynamic range. They provide 4096 potential grey levels in an image compared with only 256 grey level in the previous 8-bit instrument. Therefore, the improved signal to noise performance enables better characterization of land cover state and condition. The final products are delivered as 16-bit images scaled to 55,000 grey levels.
Landsat 8 images include nine spectral bands with a spatial resolution of 30 m for bands 1 to 7 and 9. A new ultra-blue band is useful for coastal and aerosol studies and band 9 is useful for cirrus cloud detection. The resolution of band 8 is 15 m while thermal bands 10 and 11 are useful in providing more accurate surface temperatures via 100 m resolution. Table 1 shows the differences between Landsat 7 and Landsat 8.
TABLE 1Comparison between Landsat 7 and Landsat 8 spectral bands,wavelength (nm), and detector resolution (m) [41]Landsat 7Landsat 8BandsWavelengthResolutionBandsWavelengthResolution1 (blue)0.45-0.52301 (coastal0.43-0.4530aerosol)2 (green)0.52-0.60302 (blue)0.45-0.51303 (red)0.63-0.69303 (green)0.53-0.59304 (NIR)0.77-0.90304 (red)0.64-0.67305 (SWIR 1)1.55-1.75305 (NIR)0.85-0.88306 (TIRS) 10.4-12.50606 (SWIR 1)1.57-1.65307 (SWIR 2)2.09-2.35307 (SWIR 2)2.11-2.29308 (Panchromatic)0.52-0.90158 (Panchromatic)0.50-0.68159 (circus)1.36-1.393010 (TIRS 1)10.60-11.1910011 (TIRS 2)11.50-12.51100
A different approach for rapidly characterizing soil salinity is offered by proximal sensing techniques using either visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) or portable x-ray fluorescence (PXRF) spectrometry. Fariteh et al. (2007) studied DRS to determine its capability to identify different salt minerals in addition to quantifying soil salinity levels using samples artificially treated by different salt minerals in the laboratory, as well as those collected from a field experiment. Weindorf et al. [47] tested the effectiveness of PXRF for directly quantifying of gypsum and soil salinity. Results showed a good correlation between lab data and PXRF predictions using a simple linear regression for gypsum (r2=0.88) and soil salinity (r2=0.84) with low RMSEs. Swanhart [39] used multiple linear regression to relate PXRF elemental data (Cl, S, K, Ca) to saline, coastal soils from Louisiana, USA with a r2 of 0.86 and a RMSE of 0.67 between the datasets. Early studies investigating remote sensing or hyperspectral reflectance spectroscopy mainly explored their potential for spectral characterization of different salt mineral types or for qualitative and quantitative characterization of salinity using samples artificially spiked in the laboratory [5]. Thus, the number of such studies featuring quantitative assessment of soil salinity under natural field conditions is limited.
Carbon and Nitrogen:
Both carbon and nitrogen are critical elements in soils. Soil total carbon (TC) can improve soil fertility, quality, and water retention, and ultimately maintain and increase crop production [91]. In addition, the soil carbon pool, as the largest reservoir in the terrestrial ecosystem, is 3.3 times the size of the atmospheric pool and 4.5 times the size of the biotic pool [79]. Small changes in the soil carbon pool may influence global climate change. Soil TC loss due to cultivation degrades soil fertility and quality, reduces biomass productivity, and adversely impacts water quality; depletions exacerbated by projected global warming [79, 86]. Soil total nitrogen (TN), a critical macronutrient for plant growth, is a major determinant and indicator of soil fertility and quality, and also the most commonly deficient soil nutrient [98, 87]. However, excessive nitrogen contents in soil not only lead to non-point source pollution, such as eutrophication and associated water-quality problems [61, 124], but also can be released to the atmosphere as greenhouse gases (e.g., nitrous oxide, N2O) [81]. Moreover, the C:N ratio is a good indicator of the degree of decomposition and quality of the organic matter held in the soil [53]. Soil TC is the driving force of biological activity, serving as the primary source of energy and nutrients for many soil organisms [66], and an important factor affecting nitrogen mineralization and immobilization in soils [65, 72, 80]. Soil nitrogen, as a key nutrient, can directly influence carbon sequestration in terrestrial ecosystems [95]. Therefore, spatial predictions of soil TC and TN contents are needed for a wide range of agricultural and environmental applications [91, 116].
For decades, classical laboratory-based methods have been utilized for quantifying soil TC and TN content. Two basic approaches are used to quantify TC in soils, namely, dry combustion and wet combustion [92]. Dry combustion requires separate determinations for inorganic- and organic-C, is time consuming, relatively expensive, and not adaptable to in situ determinations [99]. Wet combustion is a semi-quantitative estimate of soil carbon due to the lack of a universal conversion factor for each soil analyzed; it is time-consuming, tedious, and generates toxic waste that must be disposed of properly [92, 66]. Another relatively inexpensive and rapid technique, loss-on-ignition (LOI) has been shown to be inaccurate in some instances due to the decomposition of certain mineral fractions at high temperatures [92, 79]. The Dumas [67] (dry combustion) and [76] (wet oxidation) methods have gained general acceptance for determination of TN in the laboratory. However, both methods are time consuming, destructive to the sample being analyzed, and fail to recover some forms of N, particularly N in certain heterocyclic compounds and compounds containing N—N and N—O linkages [54]. The Dumas method is also expensive and has lower precision than Kjeldahl approaches [54]. Another method of quantification involves ion sensing electrodes, as a quick and reliable alternative to chemical-based laboratory methods for nitrate measurements. However, interference from other similar and undesired ions can be problematic; sometimes causing instability in attaining equilibrium [78]. Also, cell membranes, reference electrodes, and amplifier distortions may cause anomalous readings [78]. The disadvantages of all these traditional laboratory analysis methods are compounded by the large number of samples required for accurate assessment [84, 86]. Although these traditional methods are relatively accurate and widely accepted, they require extensive lab work and destroy the sample during processing. Therefore, there is a growing demand for rapid, cost effective, and nondestructive approaches for predicting C and N in situ. Proximal soil sensing techniques have the potential to eliminate the aforementioned constraints.
One popular proximal soil sensing technique, visible near infrared (VisNIR) spectroscopy, is quick, cost-effective, non-destructive, requires little sample preparation with no hazardous chemicals used, and is highly adaptable to automated and in situ measurements [85, 46]. Such approaches have attracted widespread interest in soil science since the 1980s [108]. The same spectra from scanning a soil with VisNIR spectroscopy can be used for the prediction of a variety of soil properties simultaneously [102], especially soil carbon. Many recent studies have been conducted on quantifying TC [114, 86], soil organic carbon, inorganic carbon [101, 89, 71] and other soil carbon fractions [115, 102] using VisNIR spectroscopy in the laboratory, in situ, or using airborne imaging spectroscopy [90, 89, 109, 70]. Comparatively fewer studies have focused on estimating soil nitrogen [103, 60, 52] through these approaches, let alone simultaneously with soil carbon. Although these studies obtained excellent results, which showed that it is a viable alternative for the routine quantitative analysis of soil carbon, lab-based VisNIR can only provide semi-quantitative estimation with residual prediction deviation (RPD)=1.5-2.0 [57]. Many factors, including moisture, particle size, mineral composition, and the presence of Fe, influence the reflectance of soils [74, 82, 99] and soil VisNIR spectra are largely non-specific, quite weak, and broad due to overlapping absorptions of soil constituents [100]. Morgan et al. [89] found VisNIR spectra alone do not provide sufficient accuracy for stand-alone C sequestration measurement, monitoring and verification. VisNIR spectroscopy alone will never provide complete soil characterization.
Another proximal soil sensing technique, x-ray fluorescence spectrometry has been used since the 1930s [20]. Given technological advances in recent years, portable x-ray fluorescence (PXRF) spectrometry has been developed and improved greatly with a number of significant advantages including minimal sample preparation, high sample throughputs, and the rapid, nondestructive, accurate, low cost, and in situ identification of many elements [112, 50]. Therefore, PXRF has become increasingly popular and been adopted by environmental consultancies, research institutions, and governmental agencies such as the US Environmental Protection Agency via Method 6200 [113], the International Organization for Standardization (ISO) [75], and the National Institute for Occupational Safety and Health (NIOSH) Method 7702 [93] for the analysis of soil and sediments. Portable x-ray fluorescence spectrometry can quantify elements from z=15 (P) through 94 (Pu) and is useful for environmental monitoring of many elements in soils and other geological materials [47, 123]. Applied to soil science, many studies have focused on metal contamination assessment using PXRF [119, 122, 96, 73], and PXRF elemental data has been used as a proxy for a wide number of soil parameters such as pH [104], cation exchange capacity [105], soil calcium and gypsum [118, 47, 125], soil texture [50], soil salinity [110], and soil horizon differentiation [120, 121]. However, PXRF cannot presently be used to quantify lighter elements [94] (e.g., Na, N, C, H, Li) given their stable electron configurations and low fluorescent energies. Few studies have attempted to predict soil carbon and nitrogen by using PXRF, given that indirect approaches to these determinations are required as direct measurements are not possible. However, Weindorf et al. [121] tried to link organic carbon content with PXRF elemental concentration while differentiating spodic horizons, and found some associations of soil carbon and nitrogen with PXRF elemental data.
Many studies have reported successful prediction of soil properties using a single instrument (e.g., VisNIR, PXRF, and so on). However, single sensors provide no robust capability to measure soil properties successfully at different sampling sites because of the complex nature of soils [117]. Wang et al. [117] predicted soil texture using Fourier transform near-infrared (NIR) spectroscopy and PXRF spectrometry with data fusion and concluded soil textural fractions predicted with sample data and sensor data fusion methods (e.g., clay, validation R2=0.83-0.86, RPD=1.94-2.39) were more accurate than those with individual sensors and individual data sets (e.g., clay, validation R2=0.61-0.74, RPD=1.56-2.36).
Petroleum Contaminated Soils:
Soil petroleum contamination is a serious environmental concern because of its neurotoxic effects on humans and animals [132]. The growth of the petroleum industry worldwide and marketing of petroleum products have resulted in countless chances for spillage. Most commonly, when an underground storage tank is removed, soil petroleum contamination is discovered which may pose an even more worrisome problem, groundwater contamination. Moreover, environmental pollution resulting from crude oil drilling has put numerous food crops under considerable risk [160]. On Apr. 20, 2010, the largest accidental marine oil spill in the history of the petroleum industry occurred following a sea-floor oil spill gusher from the Deepwater Horizon drilling rig explosion in the Gulf of Mexico south of Louisiana, USA. To date, the total costs associated with lost jobs, contaminated food and water, cleanup, restoration, and environmental damage have not been fully determined [133] and may not be for many years. Initial estimates placed the cost of damages to the Oil Company, environment, and US Gulf Coast economy at $36.9 billion [163], but later estimates by the Oil Company were closer to $41.0 billion. Undoubtedly, rapid and cost-effective means of identifying total petroleum hydrocarbon (TPH) content in contaminated soils could substantially reduce the cost involved in their restoration.
Rapid and wide scale characterization of soil petroleum contamination is not feasible with traditional gas chromatography based methods since these are prohibitively expensive, extremely laborious, time consuming, sometimes show high variability (an order of magnitude) in TPH results across commercial laboratories, lack field-portability, and warrant rigorous field sampling [140, 153].
Remote sensing tools appear to be a viable technology to provide a comprehensive solution to this problem [164]. Specifically, a mounting body of literature underlines the usefulness of visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) (350-2500 nm) as a rapid and noninvasive technique for the estimation of several soil properties simultaneously and in-situ with minimum or no sample pretreatments [13, 59, 46, 115]. Chakraborty et al. [134, 62, 135, 12] demonstrated the capability of VisNIR DRS to estimate soil petroleum contamination from a single reflectance spectrum of the contaminated soil by means of multivariate regression models. Other researchers also independently showed the robustness of VISNIR DRS models for rapidly estimating TPH and polycyclic aromatic hydrocarbons [145, 141, 162, 156]. The underlying principle is based on the diagnostic absorption bands (primarily overtones and combinations) in the VisNIR region arising from the C—H bond in hydrocarbons, helping in both qualitative and quantitative analysis of contaminated soils.
Another soil sensing technique, X-ray fluorescence spectrometry, has been used since 1930s [20]. Given technological advances in recent years, portable X-ray fluorescence (PXRF) spectrometry has been developed and improved greatly with a number of significant advantages including minimal sample preparation, high sample throughputs, and the rapid, nondestructive, accurate, low cost, and in-situ identification of many elements [112, 166, 50]. Therefore, PXRF has become increasingly popular for soil/sediment analysis with references such as Method 6200 [113] among others. PXRF can quantify elements from z=15 (P) through 94 (Pu) and is useful for environmental monitoring of many elements in soils and other geological materials [47, 123]. Applied to soil science, many studies have focused on metal contamination assessment using PXRF [119, 122, 96, 161, 73], and PXRF elemental data has been used as a proxy for a wide number of soil parameters such as pH [104], cation exchange capacity [105], soil calcium and gypsum [118, 47, 125], soil texture [50], soil salinity [110], and soil horizonation [120, 121].